Customization and optimization of SSD-based neural network model for detection of external force damage on transmission lines

Yingying Chi; Rui Liu; Wenpeng Cui; Haifeng Zhang; Yidong Yuan

doi:10.1117/12.2540376

31 July 2019 Customization and optimization of SSD-based neural network model for detection of external force damage on transmission lines

Yingying Chi, Rui Liu, Wenpeng Cui, Haifeng Zhang, Yidong Yuan

Author Affiliations +

Proceedings Volume 11198, Fourth International Workshop on Pattern Recognition; 111980R (2019) https://doi.org/10.1117/12.2540376
Event: Fourth International Workshop on Pattern Recognition, 2019, Nanjing, China

Abstract

Based on the principle of SSD (Single Shot Multibox Detector) convolutional neural network algorithm, this paper develops corresponding training strategies, and uses the source data generated under a large number of power-grid scenarios to train and generate a 100-megabyte neural network model for intelligent monitoring of external force damage on transmission lines. Using the deep compression technology, the trained neural network model is re-trained and optimized in a targeted manner to ensure a compression ratio of 30%-50% under the premise that the accuracy is not degraded. In this way, the hardware storage resource configuration is more reasonable when the model is deployed on the embedded platform.

Citation Download Citation

Yingying Chi, Rui Liu, Wenpeng Cui, Haifeng Zhang, and Yidong Yuan "Customization and optimization of SSD-based neural network model for detection of external force damage on transmission lines", Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980R (31 July 2019); https://doi.org/10.1117/12.2540376

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